English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7.5 Hours | 3.16 GB
Get Historical Prices, Fundamentals, Metrics/Ratios etc. for thousands of Stocks, Bonds, Indexes, (Crypto-) Currencies
What can be the most critical and most expensive part when working with financial data?
Pandas coding? Creating some advanced Algorithms to analyse and optimize portfolios? Building solutions for Algorithmic Trading and Robo Advising? Maybe! But very often it is … getting the Data!
Financial Data is scarce and Premium Data Providers typically charge $20,000 p.a. and more!
However, in 95% of all cases where Finance Professionals or Researchers require Financial Data, it can actually be obtained from Free or low-priced web sources. Some of them provide powerful APIs and Python wrapper packages, which makes it easy and comfortable to import the data with and into Python.
This course shows you how to get massive amounts of Financial Data from the web and provides downloadable Python coding templates (Jupyter Notebooks) for your convenience!
This course covers four different data sources and explains in detail how to install required Libraries and how to download and import the data with few lines of Python Code. You will have access to
- 60+ Exchanges all around the world
- 120,000+ Symbols/Instruments
- Historical Price and Volume Data for thousands of Stocks, Indexes, Mutual Funds and ETFs
- Foreign Exchange (FOREX): 150+ Physical Currencies / Currency Pairs
- 500+ Digital- / Cryptocurrencies
- Fundamentals, Ratings, Historical Prices and Yields for Corporate Bonds
- Commodities (Crude Oil, Gold, Silver, etc.)
- Stock Options for 4,500 US Stocks
- Fundamentals, Metrics and Ratios for thousands of Stocks, Indexes, Mutual Funds and ETFs
- Balance Sheets
- Profit and Loss Statements (P&L)
- Cashflow Statements
- 50+ Technical Indicators (e.g. SMA, Bollinger Bands)
- Real-time and Historical Data (back to 1960s)
- Streaming high-frequency real-time Data
- Stock Splits and Dividends and how these are reflected in Stock Prices
- Learn how Stock Prices are adjusted for Stock Splits and Dividends…
- … and use appropriately adjusted data for your tasks! (avoid the Pitfalls!)
- Build your own Financial Databases…
- … And save thousands of USDs!
What you’ll learn
- Importing free / low-priced Financial Data from the Web with Python
- Installing the required Libraries and Packages
- Working with powerful APIs and Python wrapper packages
- Downloading Historical Prices and Fundamentals for thousands of Stocks, Indexes, Mutual Funds and ETF´s
- Downloading Historical Prices for Currencies (FOREX), Cryptocurrencies, Bonds & more
- Saving / Storing the Data locally
- Pandas Coding Crash Course
Table of Contents
Getting Started
1 Tips How to get the most out of this Course
2 Course Overview
3 Hands-on Downloading CSV-files and import to Python
Importing Financial Data from Web Source 1
4 Intro
5 Currencies FX
6 Cryptocurrencies
7 Mutual Funds & ETFs
8 Treasury Yields
9 The Ticker Object
10 Stock Fundamentals, Meta Info and Performance Metrics
11 +++IMPORTANT NOTICE & ACTION REQUIRED (before you start with next Lecture!) +++
12 Financials (Balance Sheet, Cashflows, P&L)
13 Put Call Options
14 Streaming Real-time Data
15 Installing the required Package
16 Historical Price and Volume Data for one Stock
17 Setting specific Time Periods
18 Frequency Settings (Intraday)
19 Stock Splits and Dividends
20 Exporting to CSV Excel
21 Importing many Stocks
22 Financial Indexes
Importing Financial Data from Web Source 2
23 Intro Get your API Key
24 Currencies FX
25 Cryptocurrencies
26 Installing the required Package
27 Historical Price and Volume Data for one Stock
28 Setting specific Time Periods
29 Stock Splits and Dividends
30 Converting to DatetimeIndex
31 Frequency Settings (Intraday)
32 Real-time Data for many Stocks
33 Technical Indicators
Importing Financial Data from Web Source 3
35 Intro Register and get your API Key
36 Streaming high-frequency real-time Data (Part 1)
37 Streaming high-frequency real-time Data (Part 2)
38 Installing the required Package
39 Connecting to the APIServer
40 Currencies FX (incl. BidAsk)
41 Frequency Settings (Intraday)
42 Setting specific Time Periods
43 Stock Indexes (incl. BidAsk)
44 Commodities (incl. BidAsk)
45 Cryptocurrencies (incl. BidAsk)
Web Source 3b (for US and Canadian Residents)
46 Intro Register
47 Commands to install required packages
48 Installing the required Packages
49 Get your API Key and connect to the Server
50 Getting Historical Data
51 Frequency Settings (high-frequency Intraday Data)
52 Streaming high-frequency real-time Data
Importing Financial Data from Web Source 4
53 Intro Register and get your API Key
54 Treasury Yields
55 Stock Fundamentals, Meta Info and Performance Metrics
56 Financials (Balance Sheet, Cashflows, P&L)
57 Fundamentals and Performance Metrics for Funds & ETFs
58 Bond Data Fundamentals
59 Bonda Data Ratings
60 Bond Data Historical Prices and Yields
61 Bulk Download of Ticker Symbols for entire Exchanges
62 Bulk Download of Stock Prices, Stock Splits and Dividends
63 Introduction to the API (hands-on)
64 Getting Historical Stock Prices and Volume Data
65 Stock Splits and Dividends
66 Financial Indexes
67 Currencies FX
68 Cryptocurrencies
69 Commodities
70 Mutual Funds & ETFs
Installing Python and DownloadWorking with Templates
71 Installing Anaconda
72 How to open a Jupyter Notebook
73 Working with Jupyter Notebooks
74 Downloading and Working with Templates
Appendix 1 Pandas Crash Course
75 Intro to Tabular Data Pandas
76 Tabular Data Cheat Sheets
77 Download of Datasets (csv files)
78 First Steps (Inspection of Data, Part 1)
79 First Steps (Inspection of Data, Part 2)
80 Built-in Functions, Attributes and Methods
81 Make it easy TAB Completion and Tooltip
82 Selecting Columns
83 Selecting Rows with iloc
84 Selecting Rows with loc
85 Pandas Series
86 Importing Time Series Data from csv-files
87 Converting strings to datetime objects with pd.to datetime()
88 Initial Analysis Visualization of Time Series
89 Indexing and Slicing Time Series
90 Initial Inspection and Visualization of Financial Time Series
91 Normalizing Time Series to a Base Value (100)
92 Hands-on Importing Excel-Files to Python
What´s next
93 Get your special BONUS here!
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